Extended Data Fig. 4: Integration of RNA-seq and timeseries to investigate candidate drivers.
a, Distribution of variant allele fraction calculated from the RNA-seq data of variants detected by WGS. The number of variants represented in each boxplot is detailed in Supplementary Table 13. b, RNA-seq defined transcripts per million (TPM) of selected genes TP53 (n = 7 mutated vs. 66 WT), ATM (n = 6 mutated vs. 67 WT), and SETD2 (n = 7 mutated vs. 68 WT), according to the presence of a genomic alterations (ALT) or an absence of alteration (WT). p-values were calculated using a two-sided Welch’s t-test. c, Number of driver genes per sample for CLL samples and paired Richter’s’ syndrome (RS) samples (n = 16 vs. 16) for all 58 drivers (left panel) p = 2.4 ×10-3, the 36 known genes (central panel) p = 5.8 ×10-3 and 22 candidate genes (right panel) p = 2.1 ×10-3 (two-sided Welch’s t-test). d, Difference of number of mutated samples in Richter Syndrome (RS) samples vs paired CLL samples. Each bar indicates the absolute number of mutated samples per group. e-f, Distribution of cancer cell fractions (CCFs) in frontline samples vs. relapsed/refractory (R/R) samples (cohort 1, unpaired samples) (e), and CLL vs RS as well as frontline vs. relapsed (cohort 2 and 3, paired samples) (f). Corresponding variants are connected by a dotted line. Panels are ordered based on the direction of evolution: high stable CCFs and increasing CCFs, mixed increasing/decreasing CCFs, low/decreasing CCFs. Figures are not shown if no R/R / T2 sample carried a mutation. * indicates candidate drivers. g, Distribution of cancer cell fractions in frontline vs. relapsed (paired samples). Corresponding variants are connected by a dotted line. Panels are ordered based on the direction of evolution: high stable CCFs and increasing CCFs, mixed increasing/decreasing CCFs, low/decreasing CCFs. All boxplots show the minimum and maximum values and interquartile range and all datapoints are represented.
